## Teaching

#### Complexity Theory I

A short 12 lecture course on Computability and Complexity Theory.

#### Maths for CS I: Probability and Statistics

A first course in probability and statistics with a focus on discrete spaces.

#### Linear Algebra II

An introduction to intermediate level topics in Linear Algebra by a series of questions and answers. All materials including videos, notes, assignments is provided here. The textbooks used are also openly available.

#### Markov Chain Monte Carlo: Theory, Applications and Interdisciplinary Problems

A project oriented course on theory and applications of MCMC techniques.

#### Modern Complexity Theory (Monsoon 2020)

A first undergraduate course on Computational Complexity Theory.

#### Probability & Statistics (Monsoon 2020)

A first course in probability and statistics with a focus on discrete spaces.

#### Probabilistic Graphical Models

Probabilistic Graphical Models refers to i.) concise representations of probability distributions using graphs ii.) efficient algorithms for sampling distributions represented in such form iii.) learning these representations from data.

#### Advanced Mathematical Structures

A broad set of intermediate and advanced level topics in Algebra, Combinatorics, Probability and Graph Theory.

#### Probability & Statistics

A first undergraduate course in probability and statistics with a focus on discrete spaces.

#### Machine Learning in Natural Sciences

A project oriented course in applying machine learning techniques to solving problems in natural sciences.

#### Efficient CNNs

Surveying methods used to make deep learning models efficient.

#### Multi Object Tracking using Deep Learning

Surveying Deep learning methods used in Reinforcement Learning

#### Complexity Theory

A short 12 lecture course on Computational Complexity Theory.